/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.spark.examples.ml;

// $example on$

import org.apache.spark.ml.feature.Imputer;
import org.apache.spark.ml.feature.ImputerModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

import java.util.Arrays;
import java.util.List;

import static org.apache.spark.sql.types.DataTypes.DoubleType;
import static org.apache.spark.sql.types.DataTypes.createStructField;

/**
 * An example demonstrating Imputer.
 * Run with:
 * bin/run-example ml.JavaImputerExample
 */
public class JavaImputerExample {
    public static void main(String[] args) {
        SparkSession spark = SparkSession
                .builder()
                .appName("JavaImputerExample")
                .getOrCreate();

        // $example on$
        List<Row> data = Arrays.asList(
                RowFactory.create(1.0, Double.NaN),
                RowFactory.create(2.0, Double.NaN),
                RowFactory.create(Double.NaN, 3.0),
                RowFactory.create(4.0, 4.0),
                RowFactory.create(5.0, 5.0)
        );
        StructType schema = new StructType(new StructField[]{
                createStructField("a", DoubleType, false),
                createStructField("b", DoubleType, false)
        });
        Dataset<Row> df = spark.createDataFrame(data, schema);

        Imputer imputer = new Imputer()
                .setInputCols(new String[]{"a", "b"})
                .setOutputCols(new String[]{"out_a", "out_b"});

        ImputerModel model = imputer.fit(df);
        model.transform(df).show();
        // $example off$

        spark.stop();
    }
}
